{"id":"W4394822141","doi":"10.1371/journal.pdig.0000474","title":"Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact","year":2024,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute on Minority Health and Health Disparities; National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Gordon and Betty Moore Foundation; National Heart, Lung, and Blood Institute; Office of Naval Research; Microsoft Research; Radiological Society of North America","keywords":"Health care; Software deployment; Novelty; Work (physics); Field (mathematics); Computer science; Knowledge management; Artificial intelligence; Data science; Psychology; Engineering; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1763366,0.0009551024,0.001851904,0.004038615,0.004084759,0.03225717,0.00436162,0.01444008,0.004986049],"category_scores_gemma":[0.2311881,0.0008517726,0.0009075125,0.004486563,0.0373327,0.0807211,0.01977599,0.02239125,0.002167821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01058398,"about_ca_system_score_gemma":0.02020101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002382093,"about_ca_topic_score_gemma":0.002722386,"domain_scores_codex":[0.8849843,0.07972011,0.00625275,0.006035088,0.01977385,0.003233799],"domain_scores_gemma":[0.6349714,0.3044934,0.007856339,0.01820631,0.0279398,0.006532838],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006847019,0.00005733564,0.001100885,0.002688047,0.00006283807,0.0002026481,0.007683426,0.0007144948,0.0004678321,0.7598857,0.05273307,0.1743353],"study_design_scores_gemma":[0.00002578057,0.0001176585,0.0008520533,0.008371156,0.00004250084,0.0004002301,0.01582707,0.001065119,0.0008014321,0.5952465,0.3771651,0.00008541372],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.001979796,0.05232428,0.02739889,0.9024131,0.003961965,0.00007692527,0.00006024698,0.000101175,0.01168362],"genre_scores_gemma":[0.2904608,0.1782623,0.09827802,0.395775,0.02541879,0.0009794054,0.000245366,0.0009697486,0.009610506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8236634,"threshold_uncertainty_score":0.9325684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2066025209202274,"score_gpt":0.4608460144938039,"score_spread":0.2542434935735765,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}